摘要
虽然总的海马体积测量是阿尔茨海默病(AD)的重要标志,但是最近的证据表明,海马萎缩可能预测神经退行性疾病更敏感。神经影像学研究这一课题的论文绝大多数都集中在阿尔茨海默病和轻度认知障碍患者(MCI)之间的差异,没有考虑轻度认知障碍患者会不会转变为阿尔茨海默病。因此,本研究的目的是确定海马体积测量是否提供超过总判别这些团体的优势。海马体积测量在55例阿尔茨海默病中,32例转换和89例不转换轻度认知障碍患者(c/nc-MCI)和47名健康对照者,采用基于马尔可夫随机域嵌入改变框架基于阿特拉斯算法。探讨海马萎缩的影响在鉴别阿尔茨海默病样表型,我们使用三种分类方法:支持向量机,那起义朴素贝叶斯分类器、神经网络分类器。仅考虑总的海马体积,所有的分类模型,轻度认知障碍患者对照组和轻度认知障碍患者正常对照组之间的鉴别灵敏度达到66%。否则,考虑所有分割子域分类分析,轻度认知障碍患者对照组增加诊断的准确性从68%到72%。这种效果是强烈地依赖于萎缩的脑下托和前下托。我们的多变量分析显示差异考虑海马子域的容量的大小,被认为以阿特拉斯为基础的自动算法分割,提供了一个优势,通过海马体积区分早期阿尔茨海默病和正常对照轻度认知障碍患者。
关键词: 萎缩
Current Alzheimer Research
Title:Hippocampal Subfield Atrophies in Converted and Not-Converted Mild Cognitive Impairments Patients by a Markov Random Fields Algorithm
Volume: 13 Issue: 5
Author(s): Roberta Vasta, Antonio Augimeri, Antonio Cerasa, Salvatore Nigro, Vera Gramigna and , Matteo Nonnis, Federico Rocca, Giancarlo Zito, Aldo Quattrone, for the Alzheimer’s Disease Neuroimaging Initiative
Affiliation:
关键词: 萎缩
摘要: Although measurement of total hippocampal volume is considered as an important hallmark of Alzheimer’s disease (AD), recent evidence demonstrated that atrophies of hippocampal subregions might be more sensitive in predicting this neurodegenerative disease. The vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will or not convert in AD. For this reason, the aim of this study was to determine if measurements of hippocampal subfields provide advantages over total hippocampal volume for discriminating these groups. Hippocampal subfields volumetry was extracted in 55 AD, 32 converted and 89 not-converted MCI (c/nc-MCI) and 47 healthy controls, using an atlas-based automatic algorithm based on Markov random fields embedded in the Freesurfer framework. To evaluate the impact of hippocampal atrophy in discriminating the insurgence of AD-like phenotypes we used three classification methods: Support Vector Machine, Naïve Bayesian Classifier and Neural Networks Classifier. Taking into account only the total hippocampal volume, all classification models, reached a sensitivity of about 66% in discriminating between c-MCI and nc-MCI. Otherwise, classification analysis considering all segmenting subfields increased accuracy to diagnose c-MCI from 68% to 72%. This effect resulted to be strongly dependent upon atrophies of the subiculum and presubiculum. Our multivariate analysis revealed that the magnitude of the difference considering hippocampal subfield volumetry, as segmented by the considered atlas-based automatic algorithm, offers an advantage over hippocampal volume in distinguishing early AD from nc-MCI.
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Roberta Vasta, Antonio Augimeri, Antonio Cerasa, Salvatore Nigro, Vera Gramigna and , Matteo Nonnis, Federico Rocca, Giancarlo Zito, Aldo Quattrone, for the Alzheimer’s Disease Neuroimaging Initiative , Hippocampal Subfield Atrophies in Converted and Not-Converted Mild Cognitive Impairments Patients by a Markov Random Fields Algorithm, Current Alzheimer Research 2016; 13 (5) . https://dx.doi.org/10.2174/1567205013666160120151457
DOI https://dx.doi.org/10.2174/1567205013666160120151457 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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